Single-molecule fluorescence imaging techniques enable the detection of individual dye-labeled proteins and nucleic acids in vitro and in vivo (for example, see Walter, N. G., C. Y. Huang, A. J. Manzo, and M. A. Sobhy. 2008. Do-it-yourself guide: how to use the modern single-molecule toolkit. Nat Methods 5 (6):475-89 (Walter et al. 2008); Zhuang, X. 2005. Single-molecule RNA science. Annu Rev Biophys Biomol Struct 34:399-414 (Zhuang 2005); Weiss, S. 1999. Fluorescence spectroscopy of single biomolecules. Science 283 (5408):1676-83 (Weiss 1999); and Roy, R., S. Hohng, and T. Ha. 2008. A practical guide to single-molecule FRET. Nat Methods 5 (6):507-16 (Roy et al. 2008)). Such methods can be used in conjunction with Fluorescence Resonance Energy Transfer (FRET), where through-space energy transfer between two fluorophores—donor and acceptor—can be used to report on the distance between the two probes. More than two fluorophores may be used, such that multiple FRET pairs can interact in a given system. FRET is a spectroscopic ruler (for example, see Stryer, L., and R. P. Haugland. 1967. Energy transfer: a spectroscopic ruler. Proc. Natl. Acad. Sci., USA 58 (2):719-26 (Stryer et al. 1967)), providing a means to measure the structural properties of biological particles. Using surface-immobilization to restrict diffusion, this structural information can be followed over time, revealing structural dynamics involved in the molecular mechanisms of biological motors, transporters, sensors, signaling networks, and enzymes.
Because the observed dynamics often manifest as a sequence of dwells in distinct FRET states, single-molecule FRET (smFRET) traces are amenable to hidden Markov modeling (HMM) analysis provided that certain simplifying assumptions can be made (for example, see Rabiner, L. R. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77 (2):257-286 (Rabiner 1989)). This analysis provides a statistical framework for evaluating kinetic models that describe the energy landscape of motion (for example, see McKinney, Sean A., Chirlmin Joo, and Taekjip Ha. 2006. Analysis of Single-Molecule FRET Trajectories Using Hidden Markov Modeling. Biophys J 91 (5):1941-1951 (McKinney et al. 2006); Munro, J. B., R. B. Altman, N. O'Connor, and S. C. Blanchard. 2007. Identification of two distinct hybrid state intermediates on the ribosome. Mol Cell 25 (4):505-17 (Munro et al. 2007); Bronson, Jonathan E., Jingyi Fei, Jake M. Hofman, Ruben L. Gonzalez Jr., and Chris H. Wiggins. 2009. Learning rates and states from biophysical time series: A Bayesian approach to model selection and single-molecule FRET data. Biophys J 97 (12): 3196-3205 (Bronson et al. 2009); and Liu, Y., J. Park, K. A. Dahmen, Y. R. Chemla, and T. Ha. 2010. A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis. J Phys Chem B 114 (16):5386-403 (Liu et al. 2010)).
In aggregate, thousands of traces may provide enough statistical information to reveal subtle changes in structure and dynamics in response to ligands, drugs, or interactions with binding partners not readily apparent in individual traces (for example, see Feldman, M. B, D. S. Terry, R. B. Altman, and S. C. Blanchard. 2009. Aminoglycoside Activity Observed in Single, Pre-translocation ribosome complexes. Nature Chemical Biology 6, 54-62 (Feldman et al. 2009); and Geggier, P., R. Dave, M. B. Feldman, D. S. Terry, R. B. Altman, J. B. Munro, and S. C. Blanchard. 2010. Conformational Sampling of Aminoacyl-tRNA during Selection on the Bacterial Ribosome. J Mol Biol 399(4): 576-95 (Geggier et al. 2010)).
Analysis of smFRET data presents a problem because many current analysis methods depend on manual steps like examining each trace by eye. As a result, data analysis presents a significant bottleneck for throughput. Manual data analysis techniques can also introduce biases that that may in some cases be user dependent leading to altered or misguided interpretations of the data obtained.
In the present disclosure, we report a software platform for smFRET investigations that circumvents the throughput limits of manual analysis steps through automation.